{"title":"LPG PCA算法在医学图像中的性能分析","authors":"R. Hari Kumar, B. Vinoth kumar, S. Gowthami","doi":"10.1109/MVIP.2012.6428776","DOIUrl":null,"url":null,"abstract":"This paper presents the performance analysis of the LPG PCA algorithm in medical images. Medical images containing lot of information are often affected by noise and artifacts, which leads to the inefficient diagnosis. LPG PCA which is a statistical decorrelation technique is found to be one of the efficient methods which could be used in improving the performance of medical images. For better preservation of fine structures in an image, a pixel and its nearest neighbors are modeled as a vector variable whose training samples are selected using a moving window in the image. Such a local vector variable preservation leads to the selection of similar intensity characteristics. This method is done in two stages for improving the denoising performance. Performance analysis of this technique is found using various image quality measures.","PeriodicalId":170271,"journal":{"name":"2012 International Conference on Machine Vision and Image Processing (MVIP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Performance analysis of LPG PCA algorithm in medical images\",\"authors\":\"R. Hari Kumar, B. Vinoth kumar, S. Gowthami\",\"doi\":\"10.1109/MVIP.2012.6428776\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents the performance analysis of the LPG PCA algorithm in medical images. Medical images containing lot of information are often affected by noise and artifacts, which leads to the inefficient diagnosis. LPG PCA which is a statistical decorrelation technique is found to be one of the efficient methods which could be used in improving the performance of medical images. For better preservation of fine structures in an image, a pixel and its nearest neighbors are modeled as a vector variable whose training samples are selected using a moving window in the image. Such a local vector variable preservation leads to the selection of similar intensity characteristics. This method is done in two stages for improving the denoising performance. Performance analysis of this technique is found using various image quality measures.\",\"PeriodicalId\":170271,\"journal\":{\"name\":\"2012 International Conference on Machine Vision and Image Processing (MVIP)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2012 International Conference on Machine Vision and Image Processing (MVIP)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/MVIP.2012.6428776\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 International Conference on Machine Vision and Image Processing (MVIP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/MVIP.2012.6428776","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Performance analysis of LPG PCA algorithm in medical images
This paper presents the performance analysis of the LPG PCA algorithm in medical images. Medical images containing lot of information are often affected by noise and artifacts, which leads to the inefficient diagnosis. LPG PCA which is a statistical decorrelation technique is found to be one of the efficient methods which could be used in improving the performance of medical images. For better preservation of fine structures in an image, a pixel and its nearest neighbors are modeled as a vector variable whose training samples are selected using a moving window in the image. Such a local vector variable preservation leads to the selection of similar intensity characteristics. This method is done in two stages for improving the denoising performance. Performance analysis of this technique is found using various image quality measures.